Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/alexsds/ade-workflow/ade-evaluationnpx skills add alexsds/ade-workflow --skill ade-evaluationgit clone --depth 1 https://github.com/alexsds/ade-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alexsds/ade-workflow/ade-evaluation)<a href="https://agentmods.dev/skills/alexsds/ade-workflow/ade-evaluation"><img src="https://agentmods.dev/badge/skills/alexsds/ade-workflow/ade-evaluation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00115 | $0.02473 |
| Opus 5 | $0.00057 | $0.01236 |
| Sonnet 5 | $0.00023 | $0.00495 |
| Haiku 4.5 | $0.00012 | $0.00247 |
Grade A, and why
ade-evaluation scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| `playwright.md` | UI, web pages, user interactions | Playwright MCP server, falls back to curl | How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADE Evaluation
Overview
Adversarial evaluation methodology for the ADE workflow. Anthropic found that "out of the box, Claude is a poor QA agent" — evaluators identify real issues then self-justify approval anyway. The core failure mode is: find a problem, then explain why it's actually fine. ADE counters this with graded scoring, hard thresholds, and an adversarial stance that structurally prevents self-justification.
The evaluator exists because self-evaluation does not work. When the same agent that writes code also evaluates it, confirmation bias dominates. The agent has invested effort in the implementation and unconsciously defends it. Separating implementation (Generator) from evaluation (Evaluator) eliminates this bias. The Evaluator has no stake in the code — its only job is to find failures.
Key Principles
Adversarial stance: Approach every feature from the perspective that bugs exist. Hunt for failures, not confirmations of correctness. Start each evaluation assuming something is wrong and try to prove it.
Graded scoring: Score each criterion 1-10 with hard thresholds. Any single criterion below threshold means the feature fails. There is no averaging, no weighting override, no "close enough." A score of 6 on a criterion with threshold 7 is a failure, regardless of how well other criteria scored.
Separate evaluator: The agent that writes code must never evaluate it. Self-evaluation bias causes agents to praise their own mediocre work. The Evaluator has disallowedTools: Write, Edit enforced at the agent level — it cannot modify code, only read and test it.
No self-justification: Identifying an issue and then explaining why it is acceptable is the core failure mode Anthropic documented. If an issue exists, the feature fails. Report it clearly with specific fixes. The Generator needs accurate feedback, not encouragement.
Evaluation Workflow
When the Generator hands off a feature for review:
- Receive handoff — Read the Generator's message describing what was built, which files changed, and how to test the feature
- Load rubrics — Read rubrics from
${CLAUDE_PLUGIN_ROOT}/rubrics/and.ade/rubrics/. Match the "Applies To" section in each rubric against the feature type. Select all matching rubrics — a feature can match multiple - Load testing tools — Read testing tools from
${CLAUDE_PLUGIN_ROOT}/testing-tools/and.ade/testing-tools/. Select tools that match the feature type - Test the feature — Use the selected testing tools to verify functionality. Run the app, interact with the feature as a real user would, check API endpoints, execute test suites, take screenshots for visual verification
- Score against rubrics — For each criterion in each applicable rubric, assign a score from 1-10. Compare against the hard threshold defined in the rubric. Be honest — inflated scores waste the Generator's time
- Report results — Send a scored report to the Generator via SendMessage using the report format below
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 189 lines · 115 tokens per session scan A da8c39ace435
ade-evaluation is a skill published in the GitHub repository alexsds/ade-workflow (1 stars, last pushed 5mo ago), licensed MIT. It adds 115 tokens to every session and 2,473 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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